Prospective identification of hematopoietic lineage choice by deep learning

By Felix Buggenthin, Florian Buettner, Philipp S. Hoppe, M. Endele, M. Kroiss, Michael K. Strasser, Michael Schwarzfischer, D. Loeffler, Konstantinos D. Kokkaliaris, Oliver Hilsenbeck and 3 more. 2017

180 citations recorded by the source corpus.

Differentiation alters molecular properties of stem and progenitor cells, leading to changes in their shape and movement characteristics. We present a deep neural network that prospectively predicts lineage choice in differentiating primary hematopoietic progenitors using image patches from brightfield microscopy and cellular movement. Surprisingly, lineage choice can be detected up to three generations before conventional molecular markers are observable. Our approach allows identification of cells with differentially expressed lineage-specifying genes without molecular labeling.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Molecular Biology

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, background): “In the future, lineage tracing could be revolutionized by using autoencoders to reduce the feature space of transcriptomic or variant data followed by algorithms to learn optimal cell differentiation trajectories [222] or by feeding cell morphology and movement into neural networks [204].”
In silico labeling: Predicting fluorescent labels in unlabeled images (2018, background): “…Dong et al., 2015; Mao et al., 2015; Ron-neberger et al., 2015; Van Valen et al., 2016; Xu et al., 2016), and on segmented images of morphologically simple cells to classify cell shape (Zhong et al., 2012) and predict mitotic state (Held et al., 2010) and cell lineage (Buggenthin et al., 2017).”
Deep learning for cellular image analysis (2019, methodology): “Image classifiers have also been used to identify changes in cell state 79,80 : in a recent study, scientists used a fluorescent marker of differentiation to establish a ground truth and then trained a classifier to identify differentiated cells directly from bright-field images 81 .”